01.Introduction¶
What is MEEN 382 Basic Measurements?¶
The course's goal is to be an introductory course in the world of measurement systems. Measurement systems is a broad field that contains the categories listed below.
- Sensors
This one is relatively self-explanatory. Sensors are devices that sense a physical quantity. Some examples of a sensor are a thermistor or photoresistor. These devices respond to some physical quantity that they experience (temperature, or light) by producing some relateable output.
- Error and Uncertainty
This category has to do with how well the output of a sensor is understood. While closely related, error and uncertainty have slightly different meanings, although they are often used interchangeably. Error is how close the sensor's output is to the actual physical quantity that the sensor is experiencing. In contrast, uncertainty has to do with our understanding and expectation of a particular data point or set. Uncertainty is typically discussed in the context of a specific confidence level (e.g., the data has an uncertainty of plus or minus 5 with a 95% confidence level). On the other hand, error is typically used in how different components of a measurement system introduce error into the overall output of the system.
- Amplification
Amplification has to do with the output of a sensor. Quite often, the output of a sensor is relatively small compared to the measurement system expected range. In this situation, it is difficult for the measurement system to measure detectable changes. Thus, amplification is needed. Amplification is simply the magnifying of an output of a sensor.
- Data Acquisition and Analog to Digital Conversion
Data acquisition is the method by which the output of a sensor is recorded. A device that acquires data is called a data acquisition unit or DAQ. A computer is often utilized in the data acquisition process but is not required. Data acquisition can be as simple as someone reading a gauge and writing the gauge value down. Analog to digital conversion (ADC) is how an analog output (something that may vary to any degree, i.e., 1V to 2V and everywhere in between) is converted into a digital representation of the analog output. The analogRead(...) function of the Arduino is an example of an analog to digital conversion. The voltage applied to an analog pin is an analog signal (i.e., it varies from 0 to 5 volts and anywhere in between). The signal is converted to a digital representation (i.e., integer values from 0 to 1023). Data acquisition and analog to digital conversion are technically two separate topics. However, more often then not, a data acquisition unit comes with an analog to digital converter. The terms are, therefore, quite often used interchangeably.
- Filtering
Filtering is the method of removing unwanted information in a sensor output. Quite often, when a sensor's output is collected, there exists unwanted information with the output. This unwanted information must be removed before the output is analyzed. It is quite often due to random fluctuations in the sensor output (this is what we call noise). However, sometimes it is not just noise that ends filtering out. For example, suppose the yearly variation in the amount of radiation that the earth receives from the sun is measured. The measurement system measures the radiation all day and night for an entire year. The resulting data might look like the plot shown below in Figure 1.
There appears a large period (low frequency) sinusoidal signal with a much shorter period (higher frequency) sinusoidal signal in the plot. The large period signal (the red one) would be the desired signal, while the smaller period (the blue one) signal is due to the earth's rotation.
- Signal Processing
Signal processing is some cases, is very similar to filtering but also encompasses many other aspects that filtering does not. Signal processing entails some of the details that filtering does as in the cases of sorting out the desired signal. Signal processing also has to do with the transmission of a signal. Signal transmission could involve different communication protocols such as SPI, I2C, or 1-Wire. Signal processing also pertains to the devices being used to communicate. Device communication involves transfer rates, error checking, or merely shielding in the wires between the two communicating devices.
- Statistics
Statistics is the study and analysis of collections of data. In the context of measurement systems, it is the method in which we get useful insight out of data. It is excellent that a measurement system can measure a particular quantity, but statistics gives a glimpse into what is interesting about the particular quantity. Most commonly, statistics are used to find the mean or average of data, and the variation (or deviation) of data. Countless other statistical measures are used to gain insight into data. Statistics may also be used in estimating the uncertainties in collected data.
Course Objectives¶
There are five primary objectives for this course. They are listed below.
- Design Measurement Systems
In this course, we will look at different measurement systems and their implementation. You will also design your own measurement system capable of collecting data continuously for a set period. This is the project portion of your overall grade. The Arduino will be used to complete this objective.
- Statistical Analysis
The second objective of this course is to perform a statistical analysis of various types of data. Various data distributions will be discussed and implemented. Statistical analysis will need to be conducted for the data collected from your designed measurement system. The programming language Python will be used to complete this objective.
- Uncertainty Analysis
In addition to the statistical analysis conducted in this course, an uncertainty analysis will also be discussed and implemented for various types of measurement systems. An uncertainty analysis will need to be conducted for the data collected from your designed measurement system. Python will also accomplish this goal.
- Perform Basic Measurement Tasks
Routine laboratory tasks will be discussed throughout the course. Proper procedures for these laboratory tasks will be discussed as well as best practices for those tasks. These tasks include topics such static calibration, instrument reading, and data collection.
- Communicate
Finally, the last objective of the course is for you to communicate scientific findings properly. This objective will be completed through the use of technical memos, oral presentations, and technical reports. You will have to effectively communicate your findings from your designed measurement system in these manners.
More than anything, this course's overall objective is to exercise sound judgment when it comes to laboratory tasks.¶
You may ask how am I to know what good judgment is when it comes to laboratory tasks. That is what this course is set to accomplish.
Broad Categories of Measurements¶
There are two broad categories of measurements. The first category is
- Engineering Experimentation
Engineering experimentation comes in three phases.
a. Research Experimentation
This phase pertains to basic research to extend science where the final application may be unknown. This is the purest form on engineering experimentation. It is conducting an experiment just for the sake of learning how something behaves. For example, consider you are interested in carbon nanotubes. You believe they might possess some special trait that might be useful in a variety of applications but you don't know that for sure. To learn more about the carbon nanotubes, you setup an experiment that studies the behavior of carbon nanotubes when they are vibrating in a simply supported configuration as shown in Figure 2 below.
In this experiment, the only quantity of interest is how carbon nanotubes vibrate when different conditions are applied. These conditions might be things such as the length of the nanotube, the diameter, the magnitude of the loads applied, or the frequency of the loads applied. All that can be learned from the experiment are not directly applicable to improving a commercial product. Quite often this phase of engineering experimentation is conducted in academic research.
b. Development Experimentation
This engineering experimentation stage takes what is learned from the research experimentation and applies those insights gained to develop commercial products. Continuing with the carbon nanotube example, from previous research, it has shown that carbon nanotubes possess high strength to weight ratios (i.e., they are light and strong). Incorporating the nanotubes into various commercial products may improve their performance. Examples of products that could benefit from carbon nanotubes are bicycles and baseball bats. However, further experiments are needed to be conducted to determine the performance of these applications. Various aspects might affect the performance of the commercial product. For example, the nanotubes' orientation or how they are applied (thickness, spacing, etc.) may affect the end product. Different experiments may be conducted to determine the optimum performance of the carbon nanotubes in these products.
c. Performance Testing
Performance testing is the final stage of engineering experimentation. This stage pertains to the testing of existing products by manufacturers or independent labs, often using standards published by ASTM, ASME, SAE, etc.. Examples of this stage would be fatigue life testing of a component or engine performance. This stage occurs when a product is completely fabricated and is being sold and used by consumers. The product is then tested to see how well it performs.
The other broad category of measurements is
- Measurement in operational systems
This category is seen in many different applications.
Probably the most commonly known application of measurement in an operational system is a thermostat.
A thermostat aims to measure a space's temperature and then, based on the temperature measured and a predetermine set temperature, either turn a heater/air conditioner on or off. This type of scheme is broadly known as a control system. The thermostat is controlling the heater/air conditioner.
There are many different examples of measurement systems in operational systems.
The medical field is full of operation systems.
The human is an operational system unto itself. Numerous things might be necessary for a medical professional to monitor to provide the best care possible. Things like blood pressure, heart rate, or blood oxygen level are examples of details of a human body of interest to a medical professional.
A process plant is another common application that you will experience in your degree field.
Process plants are full of measurement systems. Process plants are not only chemical plants. Paper mills, manufacturing plants, and oil refineries are examples of different process plants you may encounter in your degree field.